CoChat
Sign Up
Cited by 18Advanced MIMO Systems OptimizationMillimeter-Wave Propagation and ModelingAdvanced Wireless Network Optimization

The DOI matches its Crossref record.

Adaptive cell selection algorithm for balancing cell loads in 5G heterogeneous networks

Open access
Gures, Emre, Shayea, Ibraheem, Sheikh, Muntasir, Ergen, Mustafa, El-Saleh, Ayman A.·2023·Alexandria Engineering Journal
DOI 10.1016/j.aej.2023.04.012
Open this paper in CoChat — free account

Abstract

Heterogeneous networks (HetNets) are a promising solution for managing the exponential increase in the number of mobile users while maintaining high data rates and coverage. HetNets consist of various cell types with different cell coverage and system capacities. However, the traffic load in HetNets is variable, uneven and random over time, leading to unequal cell loads. Some cells have excessive user presence where the competition for system resources is high, while other cells have low user presence where system resources are not fully utilised. This paper proposes a mobility load balancing algorithm that prioritises millimetre-wave (mmWave) cells in target cell selection and user association to provide load balancing in 5G HetNets and improve overall system performance. A two-step target cell selection method that considers the load level of cells and reference signal received power (RSRP) is proposed. This method prioritises mmWave cells that meet cell load level and RSRP conditions in target cell selection, taking full advantage of the unique properties of mmWave cells (enhanced network throughput, spectral efficiency and enormous bandwidth) to balance traffic loads. The HO triggering and decision-making process is independently performed for each user. In case the serving cell is overloaded, different HO procedures are applied for load balancing depending on the target cell type (macro base station (BS) or mmWave BS). The scope of the study is further expanded by applying different HO procedures according to the serving and target cell types to maintain mobility robustness in case the serving cell is not overloaded. This research proposes various system scenarios with fixed HO margin (HOM) values and fixed time-to-trigger (TTT) durations to examine the effects of HCP settings on the proposed algorithm’s performance in terms of average load level of the serving cell, throughput, spectral efficiency and call dropping ratio (CDR). The system that provides the highest overall performance is applied to the proposed algorithm. To further assess and validate the performance of the proposed algorithm, it is compared with other load balancing algorithms from the literature. The simulation results reveal that the proposed algorithm does provide noticeable enhancements in network performance in terms of load level, throughput, spectral efficiency and CDR for various mobile speed scenarios as compared to existing load balancing algorithms.

Overview

Integrity signals

Registry

Crossref · 10.1016/j.aej.2023.04.012

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 33

  1. Jiang, 2021, A heterogenous network selection algorithm for internet of vehicles based on comprehensive weight, Alexandria Eng. J., 60, 4677, 10.1016/j.aej.2021.03.037 · DOI
  2. Gures, 2020, A comprehensive survey on mobility management in 5G heterogeneous networks: Architectures, challenges and solutions, IEEE Access, 8, 195883, 10.1109/ACCESS.2020.3030762 · DOI
  3. E. Gures, I. Shayea, M. Ergen, M.H. Azmi, A.A. El-Saleh, Machine learning based load balancing algorithms in future heterogeneous networks: a survey, IEEE Access, 2022. · DOI
  4. M.A. Tunc, E. Gures, I. Shayea, A survey on IoT smart healthcare: emerging technologies, applications, challenges, and future trends, arXiv preprint arXiv:2109.02042, 2021. · arXiv
  5. Shayea, 2020, Key challenges, drivers and solutions for mobility management in 5g networks: A survey, IEEE Access, 8, 172534, 10.1109/ACCESS.2020.3023802 · DOI
  6. Gures, 2022, Fuzzy logic-based load balancing algorithm in heterogeneous networks, Workshop Microwave Theory Tech. Wireless Commun. (MTTW), 2022, 210, 10.1109/MTTW56973.2022.9942604 · DOI
  7. A. Alhammadi, W.H. Hassan, A.A. El-Saleh, I. Shayea, H. Mohamad, W.K. Saad, Intelligent coordinated self-optimizing handover scheme for 4G/5G heterogeneous networks, ICT Express, 2022. · DOI
  8. Geok, 2022, 3D RT adaptive path sensing Method: RSSI modelling validation at 4.5 GHz, 28 GHz, and 38 GHz, Alexandria Eng. J., 61, 11041, 10.1016/j.aej.2022.04.033 · DOI
  9. Budalal, 2021, Millimetre-wave propagation channel based on NYUSIM channel model with consideration of rain fade in tropical climates, IEEE Access, 10, 1990, 10.1109/ACCESS.2021.3135382 · DOI
  10. Shayea, 2018, Real measurement study for rain rate and rain attenuation conducted over 26 GHz microwave 5G link system in Malaysia, IEEE Access, 6, 19044, 10.1109/ACCESS.2018.2810855 · DOI
  11. Saad, 2021, Handover parameters optimisation techniques in 5G networks, Sensors, 21, 5202, 10.3390/s21155202 · DOI
  12. Mollel, 2020, Intelligent handover decision scheme using double deep reinforcement learning, Phys. Commun., 42, 10.1016/j.phycom.2020.101133 · DOI
  13. M.S. Mollel, S.F. Kaijage, K. Michael, Deep reinforcement learning based handover management for millimeter wave communication, 2021. · DOI
  14. Mezzavilla, 2016, An MDP model for optimal handover decisions in mmWave cellular networks, Eur. Conf. Networks Commun. (EuCNC), 2016, 100
  15. Alraih, 2022, Effectiveness of Handover Control Parameters on Handover Performance in 5G and beyond Mobile Networks, Wireless Commun. Mobile Comput., 2022, 10.1155/2022/2266282 · DOI
  16. Mismar, 2018, Partially blind handovers for mmWave new radio aided by sub-6 GHz LTE signaling, IEEE Int. Conf. Commun. Workshops (ICC Workshops), 2018, 1
  17. Aghazadeh, 2018, Cell selection for load balancing in heterogeneous networks, Wireless Personal Commun., 101, 305, 10.1007/s11277-018-5689-2 · DOI
  18. Addali, 2019, Dynamic mobility load balancing for 5G small-cell networks based on utility functions, IEEE Access, 7, 126998, 10.1109/ACCESS.2019.2939936 · DOI
  19. Hasan, 2019, Cluster-based load balancing algorithm for ultra-dense heterogeneous networks, IEEE Access, 8, 2153, 10.1109/ACCESS.2019.2961949 · DOI
  20. Ghatak, 2018, Coverage analysis and load balancing in HetNets with millimeter wave multi-RAT small cells, IEEE Trans. Wireless Commun., 17, 3154, 10.1109/TWC.2018.2807426 · DOI
  21. Han, 2020, User association for load balance in heterogeneous networks with limited CSI feedback, IEEE Commun. Lett., 24, 1095, 10.1109/LCOMM.2020.2973090 · DOI
  22. Li, 2017, User association for load balancing in vehicular networks: An online reinforcement learning approach, IEEE Trans. Intell. Transport. Syst., 18, 2217, 10.1109/TITS.2017.2709462 · DOI
  23. S.S. Mwanje, A. Mitschele-Thiel, A q-learning strategy for lte mobility load balancing, in: 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), 2013, pp. 2154-2158. · DOI
  24. Pouyanfar, 2018, A survey on deep learning: Algorithms, techniques, and applications, ACM Comput. Surv. (CSUR), 51, 1
  25. Moysen, 2018, From 4G to 5G: Self-organized network management meets machine learning, Comput. Commun., 129, 248, 10.1016/j.comcom.2018.07.015 · DOI
  26. Kiran, 2021, Deep reinforcement learning for autonomous driving: A survey, IEEE Trans. Intell. Transport. Syst.
  27. K. Attiah, K. Banawan, A. Gaber, A. Elezabi, K. Seddik, Y. Gadallah, et al., Load balancing in cellular networks: A reinforcement learning approach, in: 2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC), 2020, pp. 1-6. · DOI
  28. Wang, 2021, Trajectory design and reSource allocation for UAV energy minimization in a rotary-wing UAV-enabled WPCN, Alexandria Eng. J., 60, 1787, 10.1016/j.aej.2020.11.027 · DOI
  29. A. Madelkhanova, Z. Becvar, Optimization of cell individual offset for handover of flying base station, in: 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), 2021, pp. 1-7. · DOI
  30. Hu, 2019, An intelligent uav deployment scheme for load balance in small cell networks using machine learning, IEEE Wireless Commun. Network. Conf. (WCNC), 2019, 1
  31. Gudmundson, 1991, Correlation model for shadow fading in mobile radio systems, Electron. Lett., 27, 2145, 10.1049/el:19911328 · DOI
  32. Sklar, 1997, Rayleigh fading channels in mobile digital communication systems. I. Characterization, IEEE Commun. Magaz., 35, 90, 10.1109/35.601747 · DOI
  33. Shayea, 2020, Individualistic dynamic handover parameter self-optimization algorithm for 5G networks based on automatic weight function, IEEE Access, 8, 214392, 10.1109/ACCESS.2020.3037048 · DOI

Cited by

Sorted by citations

Metadata

Authors
Gures, E., Shayea, I., Sheikh, M., Ergen, M., & El-Saleh, A. A.
Affiliations
  • Istanbul Technical University (TR)
  • King Abdulaziz University (SA)
Corresponding author
Shayea, Ibraheem
ORCID
Venue
Alexandria Engineering Journal
Publisher
Elsevier BV
Published
2023-06
Volume
72
Pages
621-634
ISSN
1110-0168 (ISSN-L)
DOI
10.1016/j.aej.2023.04.012
OpenAlex
W4366979890
Cited by
18
References
0
Citations by year
2026: 3 · 2025: 7 · 2024: 4 · 2023: 4
FWCI
1.34
Type
journal article
Language
en
Open access
Licence
Locations
  • Alexandria Engineering Journal (publishedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Istanbul Technical University Academic Open Archive (Istanbul Technical University) (submittedVersion)
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced MIMO Systems Optimization
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Millimeter-Wave Propagation and Modeling
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Network Optimization
Keywords
heterogeneous networks, 5G, load balancing, handover, cell selection, user association, RSRP, handover margin, time-to-trigger, throughput, spectral efficiency, mobility management

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review